This paper describes CAiRE’s submission to the unsupervised machine translation track ofthe WMT’19 news shared task from Germanto Czech. We leverage a phrase-based statistical machine translation (PBSMT) modeland a pre-trained language model to combine word-level neural machine translation (NMT) and subword-level NMT models without using any parallel data. We propose to solve the morphological richness problem of languages by training byte-pair encoding (BPE) embeddings for German and Czech separately, and they are aligned using MUSE (Conneau et al., 2018). To ensure the fluency and consistency of translations, a rescoring mechanism is proposed that reuses the pre-trained language model to select the translation candidates generated through beam search. Moreover, a series of pre-processing and post-processing approaches are applied to improve the quality of final translations.
Recommended citation: Liu, Z., Xu, Y., Winata, G. I., & Fung, P. (2019). Incorporating Word and Subword Units in Unsupervised Machine Translation Using Language Model Rescoring. Proceedings of the Fourth Conference on Machine Translation.